Identification Using Border Approaches and IVs

Xing  Li; Wesley R. Hartmann; Tomomichi  Amano

Working Paper | 2019

Identification Using Border Approaches and IVs

by Xing Li, Wesley R. Hartmann and Tomomichi Amano

Abstract

We document that recent quasi-experimental strategies for identifying advertising effects can be derived from a model in which ad decisions are made at a more aggregate level than conversion is measured. Next, we show that the identifying variation in one of these strategies, the Border Approach, is conceptually similar to what are commonly known as Waldfogel IVs. We compare these, as well as supply-side instruments and fixed effects, in a data set on advertising in US presidential elections. Both border approaches and IVs are known to sacrifice statistical power and they do, but not by enough to affect statistical significance, in this application. The Waldfogel IVs are much more powerful than the supply-side IVs and, when combined, the standard errors are substantially reduced. Each IV estimator has the potential to produce a local average treatment effect that weights aggregate markets differently. Estimates suggest differences may exist, but they are not significant. When both IVs are combined, the point estimate is identical to a fixed effect estimate that is likely to be unbiased. The Border Approach can produce local effects at the disaggregate level when border and non-border regions differ. We find evidence of a statistically signficant difference when analysis is restricted to those counties where identifying assumptions are more plausible. The point estimate drops to nearly zero and becomes insignificant despite a standard error that is as small as the lowest IV standard error. We suspect local estimate concerns are greater for the Border Approach because it identifies advertising effects that exclude the high population counties in all markets, whereas IVs may weight each market differently but include counties of all types within each market.

Keywords: advertising effects; Advertising; Decision Making; Political Elections; Analysis;

Language: English

Format: Print

SSRN

Citation:

Li, Xing, Wesley R. Hartmann, and Tomomichi Amano. "Identification Using Border Approaches and IVs." Working Paper, June 2019.

Machine learning is bringing us self-driving cars, improved medical diagnostics, and machine translation, but can it improve marketing decisions? It can. Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to rich media such as text, images, audio, and video. Examples include identification of customer needs from online data, accurate prediction of consumer response to advertising, personalized pricing, and product recommendations. But without a soul, the applications of machine learning are limited. Consumer behavior and competitive strategies are nuanced and richly described by formal theory. To learn across applications, to be accurate for “what-if” and “but-for” applications, and to advance knowledge, machine learning needs theory and a soul. The brightest future is based on the synergy of what the machine can do well and what humans do well. We provide examples and predictions for the future.

Othellonia surprised the Japan’s mobile game market by supporting the launch of the game with an unusual approach: Offline community management. The management team favored a slow and steady launch entirely focused on bringing very loyal users instead of attracting too many users at once. Doing so had positioned Othellonia among the mobile games with the most loyal customer base and with monetization rates above the levels usually observed in the industry. However, after a few years of steady growth, strong loyalty from its users and healthy monetization, recent acquisition, engagement and retention metrics were worrisome for Yu Sasaki and Taku Kojo (KJ). Should the team invest in the promotional activities that drove acquisition in the past? Should they rely on the loyal base of players to keep growing the game? How could they increase user engagement without hurting monetization?

Faculty & Research

Identification Using Border Approaches and IVs

Abstract

Citation:

About the Author

More from the Author